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I. Logistic Analysis The logit model where multiple possible outcomes exist can be extended to a multinomial model referred to as a generalized or baseline-category logit model of the form (McFadden, 1974): Log(Pr(Y=i|x)/Pr(Y=k+1|x)) = α i + β’ i xi = 1,....,k α i = the intercept parameters, and β i = the vector of the slope parameters.

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Analysis of Maximum Likelihood Estimates for the Probability that a Fish will be Landed in a Given Size Category Extra-Fishery Variables Parameter DF Estimate Standard Wald Error Chi-Square Pr > ChiSq sfldp

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II. Quick Assessment Method The first model (m1) predicts the number of fish landed (Type A + B1 fish) in a state that have been intercepted, identified, measured, and in some cases weighted by observers (TotSFLnmbr). The second model (m2) predicts the total number of fish (Type A+B1+B2 fish) reported to observers by anglers who did not necessarily allow them to be identified, measured, and weighted by observers (TotSFLnd).

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QAM: Scatter Plot Two scatter plots at the end of the program provide a comparison of the actual and predicted values of these two dependent variables. These plots indicate that most predicted values fall within narrow bands around the actual values of the variables; this reflects the coefficient of determination of 76.7 and 76.8 percent, respectively.

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Summary This model is a simple application of time proven methods of dealing with imperfect information in a marketplace or natural environment. While the concepts are simple, their actual application is complex. A step by step user guide is provided in the appendix attached. The programs in steps I to VII are used if the existing data set is to be modified for another species of recreationally harvested fish. These steps will update the database needed to estimate a new sets of coefficients for use in a policy analysis of any existing or proposed fishery management regulations.